Histogram-based image segmentation

ABSTRACT

Systems, apparatuses, and/or methods may provide for segmenting an image by generating a histogram of its pixel values, dividing the histogram into class intervals, and then iteratively computing new, shifted weighted means and shifted class interval boundaries for the class intervals until a predetermined level of convergence to a limit is obtained. The pixels may then be updated to a last weighted mean for class intervals to which they belong, providing segmentation. Similarly, any data may be segmented to provide computational efficiency.

BACKGROUND

Applications may utilize image analysis, which may be computationallydemanding on hardware resources. Image analysis segmentation, which mayinvolve subdividing an image into constituent regions or objects, may bean approach to reduce the demands placed on the hardware resources.Thus, image segmentation may be used in a variety of applications thatutilize image analysis such as, for example, computer visionapplications, object tracking, object identification, etc.

Moreover, segmentation may be useful in an analysis and/or amanipulation of data sets, including image data. In one example when animage is divided into foreground and background, segmentation may beuseful to prevent a portion of an object from being classified asbelonging to the background while another portion of the same object isclassified as belonging to the foreground. Segmentation, however, may becomputationally demanding. Thus, performance of an application andsuitability of available hardware to run the application may be impactedby an increase in speed and/or efficiency of segmentation performed forimage data, other forms of data, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

The various advantages of the embodiments will become apparent to oneskilled in the art by reading the following specification and appendedclaims, and by referencing the following drawings, in which:

FIG. 1 is a block diagram of an example of an image analysis system thatincludes a histogram-based mean shift segmenter according to anembodiment;

FIG. 2 is a flowchart of an example of a method of histogram-based imagesegmentation according to an embodiment;

FIG. 3A is an example of a histogram of pixels in an image to besegmented according to an embodiment;

FIG. 3B is an example of a graph showing final values of the pixels inFIG. 3A after mean shift segmentation according to an embodiment;

FIG. 4 is a block diagram of an example of a mean shift segmenteraccording to an embodiment;

FIGS. 5A-5B are example images before mean shift segmentation and aftermean shift segmentation according to an embodiment;

FIGS. 6-8 are block diagrams of an example of an overview of a dataprocessing system according to an embodiment;

FIG. 9 is a block diagram of an example of a graphics processing engineaccording to an embodiment;

FIGS. 10-12 are block diagrams of examples of execution units accordingto an embodiment;

FIG. 13 is a block diagram of an example of a graphics pipelineaccording to an embodiment;

FIGS. 14A-14B are block diagrams of examples of graphics pipelineprogramming according to an embodiment;

FIG. 15 is a block diagram of an example of a graphics softwarearchitecture according to an embodiment;

FIG. 16 is a block diagram of an example of an intellectual property(IP) core development system according to an embodiment; and

FIG. 17 is a block diagram of an example of a system on a chipintegrated circuit according to an embodiment.

DETAILED DESCRIPTION

FIG. 1 shows a block diagram of an embodiment of a system 10 to analyzean image using image segmentation. In the illustrated example, thesystem 10 includes an application 11 that may be, for example, anapplication that employs object recognition, an application thatperforms object tracking in motion video, and so forth. The application11 may invoke an image analyzer 12, which may provide image segmentationto facilitate tasks required by the application 11. In the illustratedexample, the image analyzer 12 includes enhanced photography middleware14 having a mean shift segmenter 16, discussed in detail below.

The image analyzer 12 further includes various forms of middleware 18a-18 d that may implement image analysis tasks such as, for example,hand gesture recognition, facial detection, object ranging, and/or othertasks to facilitate an analysis of an image and/or that may be useful tothe application 11. The image analyzer 12 may further include a depthcamera manager (DCM) 20 to manage streams of image data from camerahandlers 22 provided by a color sensor 24 and a depth sensor 26 of oneor more image capture devices (e.g., two-dimensional camera,three-dimensional camera, etc.).

Execution of the application 11 may be computationally demanding for ahardware system, wherein performance of the application 11 and/or of thehardware system may appear to a user as deficient. Accordingly, imagesegmentation may be performed to facilitate the execution of theapplication 11 and/or the operation of the hardware system.Additionally, segmentation may be useful in of itself, and/or as part ofanother process. For example, segmenting an object may be a first stepin performing an operation on the object (e.g., in a data representationof the object, etc.). Thus, embodiments disclosed herein may efficientlysegment an image or other data set, and/or may allow hardware toaccomplish application tasks that may otherwise be impractical from auser's point of view.

FIG. 2 shows a flowchart of an example of a method 30 of segmenting animage using a histogram-based mean shift. The method 30 may beimplemented in one or more modules as a set of logic instructions storedin a machine- or computer-readable storage medium such as random accessmemory (RAM), read only memory (ROM), programmable ROM (PROM), flashmemory, etc., as configurable logic such as, for example, programmablelogic arrays (PLAs), field programmable gate arrays (FPGAs), complexprogrammable logic devices (CPLDs), as fixed-functionality logichardware using circuit technology such as, for example, applicationspecific integrated circuit (ASIC), complementary metal oxidesemiconductor (CMOS) or transistor-transistor logic (TTL) technology, orany combination thereof. For example, computer program code to carry outoperations shown in the method 30 may be written in any combination ofone or more programming languages, including an object orientedprogramming language such as C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. Moreover, the method 30 may be implemented usingany of the herein mentioned circuit technologies.

An explanation of the method 30 is provided with reference to FIG. 2 andFIGS. 3A-3B. Referring to FIG. 2, illustrated processing block 32 beginsto execute an image segmentation task, and illustrated processing block34 generates a histogram of pixels in an image. In one example, thehistogram tallies a number of pixels in the image having a given pixelvalue, where the pixel values may be reflective of a trait and/or aquality that may be of interest to an application. For example, thetrait may correspond to brightness, intensity, color, distance or depth,disparity, and so forth. In image analysis, for example, a disparityimage may depict a difference in images or data provided by twostereoscopic cameras or sensors. Generally, the term “disparity” mayrefer to a data set that represents the difference between two otherdata sets.

Referring to FIG. 3A, an example of a histogram is shown that may begenerated of a relatively simple image (e.g., in terms of a number ofpixels considered). In the illustrated example, the horizontal axispresents twelve possible pixel values that a pixel may have, rangingfrom 1 through 12, and the vertical axis presents the frequency of theiroccurrence in the image. Thus, for example, pixels having a pixel valueof 1 occur three times, pixels having a pixel value of 2 occur 6 times,and so forth. Moreover, each pixel may have an address in the startingimage, and while not illustrated, the pixel address and its value may bestored in a memory location for every pixel in the image.

In the illustrated example, the number of pixels (37) and pixel values(12) considered have been kept to a small number for purposes ofillustration. In most applications, however, the histogram may be“dense,” having many more pixel values along its horizontal axis andencompassing many more pixels in total. For example, a Video GraphicsArray (VGA) image may have 640 rows of pixels×480 columns of pixels, or307,200 pixels in total, and a palate that may be hundreds or morelevels deep (even in monochrome). In the illustrated example, each pixelvalue may correspond to a bin of width 1, in which each bin containsevery occurrence of pixels having a particular pixel value. In otherembodiments, however, bin widths of greater than 1 may be employed. Forexample, a first bin may include all pixels having pixel values of 1-2,a second bin may include all pixels having pixel values of 3-4, and soforth. In some embodiments, pixel values may be scaled to fall between 0and 1, wherein the bins may have a fractional width (e.g., 0.001, etc.).

Referring back to FIG. 2, illustrated block 36 divides the histograminto a plurality of evenly spaced class intervals of width W. Theparticular number of class intervals used may be driven by anapplication. For example, if the application calls for determiningobject depth (i.e., distance), dividing pixel values corresponding todepth across a range of 1-1000 cm (10 meters) into 12 class intervalsmay be sufficient, depending on the level of granularity called for bythe application. As shown in FIG. 3A, the histogram has been dividedinto two class intervals. A first class interval 64 encompasses pixelvalues 1-6, wherein the upper value (6) and lowest value (1) may defineclass interval boundaries for the first class interval 64. Similarly, asecond class interval 66 encompasses pixel values 7-12 and has classinterval boundaries 7 and 12. While each class interval has the samewidth W, in some embodiments each class interval may have its own classwidth. In further embodiments, empty or nearly empty boundary bins maybe truncated from a class interval.

Referring back to FIG. 2, illustrated block 38 defines an initial meanfor each class interval. The initial mean may be a midpoint or median ofthe pixel values in the corresponding class interval. In someembodiments, the initial mean may be an unweighted mean of the pixelvalues in the class interval, or may be some other statistical measureof centrality or spread of pixel values in the class interval (e.g., amodified median, a weighted mean, etc.). As shown in FIG. 3A, theinitial mean of the first class interval 64 is an unweighted arithmeticmean of the pixel values assigned to the first class interval 64:Initial Mean=(1+2+3+4+5+6)/6=3.5

Similarly, the initial mean of the second class interval 66 is anunweighted arithmetic mean of the pixel values assigned to the secondclass interval 66:Initial Mean=(7+8+9+10+11+12)/6=9.5

Referring back to FIG. 2, illustrated block 40 computes a weighted meanWM of the bins in each class interval, in effect “shifting” the mean foreach class interval to a new value. In the example shown in FIG. 3A,each value along the horizontal axis of the histogram is its own bin ofbin width=1. As noted above, however, in other embodiments bins mayinclude more than one pixel value. The weighted mean WM of a given classinterval may be computed as follows:WM=(Σ_(i) w _(i) *x _(i))/Σ_(i) w _(i)  (Eqn. 1)

wherein i indexes values in the class interval such that x_(i) is theith pixel value in the class interval and w_(i) is a weighting functionassociated with the ith pixel value. If the bins that contain the pixelvalues have a width greater than 1, the ith weighting function may beassociated with the ith bin rather than the individual pixel valuesx_(i).

The weighting function may be the number of pixels in a bin having apixel value x_(i). In one example where the bins have a bin width=1, asshown in FIG. 3A, the weighting function may be the number of pixelshaving a given pixel value. Applying that weighting function to the datashown in FIG. 3A, a weighted mean of 2.68 for class interval 64 and aweighted mean of 10.11 for class interval 66 may be computed.

In some embodiments, the weighting function may be some otherstatistical measure of the spread, average, mode, distribution, numberof pixel values, and so forth. In other embodiments, the weightingfunction may be a step function that gives a weight of 0 if, forexample, the number of pixels in a bin is less than a threshold, and 1if the number of pixels in a bin is equal to or greater than thethreshold.

Referring back to FIG. 2, a determination may be made at processingblock 42 whether the newly computed class mean for the class intervaldiffers from the previously computed mean (e.g., whether itself aweighted mean or the initial mean) by more than some predetermined limitL. The choice of value for L may represent a tradeoff between accuracyand performance, with small values for L corresponding to greateraccuracy and larger values for L providing faster performance, possiblyat the cost of lower accuracy. In some embodiments, a small value for Lmay be selected in conjunction with limitations to the number ofiterations permitted.

If the determination at block 42 is YES, then illustrated processingblock 44 updates the most recently computed mean of the class intervalto WM. Illustrated processing block 46 updates the boundaries of theclass interval to the new weighted mean plus-or-minus one half the widthof the class interval, as follows:Updated Class Boundaries=(WM−W/2),(WM+W/2)  (Eqn. 2)

Control then passes back to block 40, wherein a new weighted mean may becomputed for the class interval. The effect is to shift the mean of eachclass interval to a new mean based on a newly computed weighted mean,and to shift the class boundaries of the class intervals.

If a determination is made at block 42 that the newly computed classmean WM does not differ from the previously computed mean by more than L(i.e., NO), then the two most recently computed means for the classinterval are judged to have converged, and a determination may be madeat processing block 48 if there are additional class intervals in thehistogram to consider. If so, then control passes to block 40 forcomputation of a weighted mean for the next class interval as before. Ifthere are no remaining class intervals, then a next phase of the method30 reassigns values associated with the pixels.

Illustrated processing block 50 reads a pixel and illustrated processingblock 52 determines the class interval (i.e., first class interval,second class interval, etc.) that the pixel initially corresponds to.Illustrated processing block 54 updates the pixel value of the pixel tothat class interval's final shifted mean value, which may be the finalweighted mean computed at block 40 for that class interval. Illustratedprocessing block 56 determines if there are more pixels to consider, andif so, control passes back to block 50. If there are no further pixelsto consider, the process ends at illustrated block 58. The updated pixelvalues may be used to generate a segmented image.

Using a limit of L=0.1, the method 30 converges to final weighted meansof 2.61 and 10.2 for the pixel values associated with class intervals 64and 66, respectively, shown in FIG. 3A. Thus, in this example, allpixels having initial pixel values of 1-6 are assigned final pixelvalues of 2.61, and all pixels having initial pixel values of 7-12 areassigned final pixel values of 10.2, as shown at segment 67 and segment69, respectively, in FIG. 3B. (Neither segment in this example is shownas including pixels having initial pixel values of 6 or 7 since pixelvalues of 6 or 7 do not appear in the initial image and have beentruncated from the segments.) The number of segments in this example isthe same as the number of class intervals employed—2. More generally,the number of class intervals into which the histogram has been dividedmay correspond to the number of segments produced by the method 30.

In some embodiments, boundaries and ranges of the class intervals may betruncated, rounded to the nearest integer, or extended for convenienceand/or to exclude boundary bins that are deemed not to have a sufficientamount of pixels to merit consideration. Fractional pixel values may betruncated, rounded up or down, and/or weighted differently, as mayfractional class boundaries. For example, in the example depicted inFIG. 3B, the first class interval 64 may have a final computed range of1-5.61, and the second class interval 66 may have a final computed rangeof 7.2-12, which may be truncated to 1-5 and 8-12 respectively as shownin FIG. 3B, or they may be extended to encompass the fractional valuesand have final computed ranges of 1-6 and 7-12 respectively.

Advantageously, characterizing an image via a histogram and thendividing the histogram into class intervals may allow for pixels to beaggregated together by class interval and means to be computed on aclass interval basis. Computational efficiently may be increasedrelative to computing means about neighborhoods of each individual pixelseparately. Techniques disclosed herein such as the method 30 may havean order of complexity of O(N+k*M), wherein N is the number of pixels inan image, k is the average number of iteration steps to convergence, andM is the number of bins. Also, the image may be read only twice; namely,once for computing the histogram and once when updating the pixel valuesto generate a new, segmented image. Other operations discussed above(e.g., shifting boundaries) may be performed using histogram classintervals that may be substantially more computationally compact thanalternatives.

The method 30 need not be sensitive to a spatial distance between pixelsand may also be performed in cases where spatial locality is of lesserimportance. One of the possible ways to implement the foregoing approachmay be as follows.

FIG. 4 shows an example of an embodiment of a mean-shift segmenter 70that may be used to segment an image. The mean-shift segmenter 70 maygenerally implement one or more aspects of the method 30 (FIG. 2),already discussed. In the illustrated example, an input image 71 ispassed to the mean-shift segmenter 70, wherein a pixel value determiner72 reads each pixel in the image and determines its corresponding pixelvalue. The pixel values are then fed to a histogram generator 73, whichgenerates a histogram of the pixel values, divides the histogram into anumber of class intervals, and defines boundaries for each classinterval.

A mean determiner 74 computes initial means for each class interval, andsubsequently computes weighted means for each class interval (ifneeded). A class interval mean updater 75 updates the mean associatedwith each class interval, and a class interval boundary updater 76updates the class interval boundaries, discussed above. When final meanvalues for each class interval are provided, the pixels are updated tothe final mean values by a pixel value updater 77. The updated pixelsmay then be used to generate a segmented image 80.

The elements depicted in FIG. 4 may be varied in arrangement and task.For example, the histogram generated may, in an alternative embodiment,include a class interval mean updater and a class interval boundaryupdater. In another example, the pixel value determiner may be part of acamera.

FIGS. 5A-5B illustrate an effect of segmentation on an image. The imagein FIG. 5A is not segmented, and FIG. 5B shows a segmented version ofthe same image. There are 8 segments in the segmented image in thisexample, which may correspond to the division of the pixels in FIG. 5Ainto 8 class intervals during a segmentation process.

In some embodiments, pixels may have single dimensional values. Forexample, a single dimensional value associated with a pixel may be ascalar, such as depth (the distance of an object in the image from thecamera capturing the image). The techniques disclosed herein may be usedin other embodiments in which each pixel value is multidimensional ormultispectral. For example, if the pixel value is color, then a pixelvalue may include intensities of red, green, and blue (RGB values, whichare used in the RGB model) or other values indicative of color. Examplesof other models that may be used include the HSI model (hue, saturation,intensity), the CMY model (cyan, magenta, yellow), and the CMYK model(cyan, magenta, yellow, and black). In each case, the pixel value ismultispectral, i.e., multidimensional. Thus, pixel values may berepresented in a suitable multidimensional space. For example, in thecase of the RGB model, pixel values may map onto a color space whosedimensions are intensities of red, green, and blue.

In the case of the RGB model, the range of colors that may berepresented by the pixel values may be an RGB space of voxels, and aparticular pixel value may be a vector in the form of (r,g,b) extendingfrom the origin (zero intensity) to the particular values for red,green, and blue indicated by r,g, and b coordinates in the RGB space. Ahistogram may be constructed by associating each voxel in this RGB spacewith a number indicating a number of pixels in the image having thatvoxel's RGB value. An individual voxel may be taken to be its own binor, in some embodiments, each bin may represent its own group of voxels(e.g., a cube of voxels in RGB space). However defined, the bins may begrouped together into class intervals of larger cubes or other shapescontaining bins. In the case where a class interval is in the shape of acube, the faces of the cube may be the class interval's boundaries.

Embodiments may extend to the multispectral case by forming a histogramof the multidimensional values that each pixel may take on andpartitioning the histogram into evenly spaced class intervals. Aninitial mean for each class interval may be a centroid of a classinterval, a median, average, weighted average, or derived using anotherconvenient statistical measure. A weighted mean WM of a given classinterval may be computed as a vector as follows:WM=(Σ_(i) w _(i) *x _(i))/Σ_(i) w _(i)  (Eqn. 3)

wherein i indexes values in a class interval such that x_(i) may be avector in the histogram's RGB space pointing to a particular bin or RGBvalue. Here, w_(i) may be a weighting function associated with the ithbin, and may be a number of pixels in the bin, although other weightingfunctions may be used.

In some embodiments the weighting function may have components that varywith the component of the x_(i) vectors. Also, a separate histogram maybe computed for each color. Each separate histogram may then form thebasis of its own set of calculations, including initial mean, classinterval boundaries, weighted mean computations, shifted means, and/orshifted boundaries.

In the multispectral case, a test for convergence may include computinga weighted vector mean WM for a class interval, subtracting a previousvector mean computed for that class interval, testing to determine if amagnitude of a resulting difference vector is greater than a scalarquantity L, and proceeding as above to shift the mean to a new vectorvalue. The boundaries of the class interval may be updated to WMplus-or-minus vectors along each axis of the space having half amagnitude of a starting width of the class interval.

The foregoing techniques may be scaled to pixels having N-dimensionalpixel values. In addition, the foregoing techniques are not limited toimage processing, and may be applied to the segmentation of data setsgenerally. For example, embodiments may be used to analyze data setsrelating to sales figures, velocities, distances, stock marketvaluations, genomic data, economic data, and so forth.

System Overview

FIG. 6 is a block diagram of a processing system 100, according to anembodiment. In various embodiments the system 100 includes one or moreprocessors 102 and one or more graphics processors 108, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 102 or processorcores 107. In on embodiment, the system 100 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 100 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 100 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 100 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 100 is a television or set topbox device having one or more processors 102 and a graphical interfacegenerated by one or more graphics processors 108.

In some embodiments, the one or more processors 102 each include one ormore processor cores 107 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 107 is configured to process aspecific instruction set 109. In some embodiments, instruction set 109may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 107 may each process adifferent instruction set 109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 107may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 102 includes cache memory 104.Depending on the architecture, the processor 102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 102. In some embodiments, the processor 102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 107 using knowncache coherency techniques. A register file 106 is additionally includedin processor 102 which may include different types of registers forstoring different types of data (e.g., integer registers, floating pointregisters, status registers, and an instruction pointer register). Someregisters may be general-purpose registers, while other registers may bespecific to the design of the processor 102.

In some embodiments, processor 102 is coupled to a processor bus 110 totransmit communication signals such as address, data, or control signalsbetween processor 102 and other components in system 100. In oneembodiment the system 100 uses an exemplary ‘hub’ system architecture,including a memory controller hub 116 and an Input Output (I/O)controller hub 130. A memory controller hub 116 facilitatescommunication between a memory device and other components of system100, while an I/O Controller Hub (ICH) 130 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 116 is integrated within the processor.

Memory device 120 can be a dynamic random access memory (DRAM) device, astatic random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment the memorydevice 120 can operate as system memory for the system 100, to storedata 122 and instructions 121 for use when the one or more processors102 executes an application or process. Memory controller hub 116 alsocouples with an optional external graphics processor 112, which maycommunicate with the one or more graphics processors 108 in processors102 to perform graphics and media operations.

In some embodiments, ICH 130 enables peripherals to connect to memorydevice 120 and processor 102 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 146, afirmware interface 128, a wireless transceiver 126 (e.g., Wi-Fi,Bluetooth), a data storage device 124 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 140 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 142 connect input devices, suchas keyboard and mouse 144 combinations. A network controller 134 mayalso couple to ICH 130. In some embodiments, a high-performance networkcontroller (not shown) couples to processor bus 110. It will beappreciated that the system 100 shown is exemplary and not limiting, asother types of data processing systems that are differently configuredmay also be used. For example, the I/O controller hub 130 may beintegrated within the one or more processor 102, or the memorycontroller hub 116 and I/O controller hub 130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 112.

FIG. 7 is a block diagram of an embodiment of a processor 200 having oneor more processor cores 202A-202N, an integrated memory controller 214,and an integrated graphics processor 208. Those elements of FIG. 7having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor200 can include additional cores up to and including additional core202N represented by the dashed lined boxes. Each of processor cores202A-202N includes one or more internal cache units 204A-204N. In someembodiments each processor core also has access to one or more sharedcached units 206.

The internal cache units 204A-204N and shared cache units 206 representa cache memory hierarchy within the processor 200. The cache memoryhierarchy may include at least one level of instruction and data cachewithin each processor core and one or more levels of shared mid-levelcache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or otherlevels of cache, where the highest level of cache before external memoryis classified as the LLC. In some embodiments, cache coherency logicmaintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or morebus controller units 216 and a system agent core 210. The one or morebus controller units 216 manage a set of peripheral buses, such as oneor more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 210 provides management functionality forthe various processor components. In some embodiments, system agent core210 includes one or more integrated memory controllers 214 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 202A-202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 202A-202N during multi-threaded processing. System agentcore 210 may additionally include a power control unit (PCU), whichincludes logic and components to regulate the power state of processorcores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphicsprocessor 208 to execute graphics processing operations. In someembodiments, the graphics processor 208 couples with the set of sharedcache units 206, and the system agent core 210, including the one ormore integrated memory controllers 214. In some embodiments, a displaycontroller 211 is coupled with the graphics processor 208 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 211 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 208 or system agent core 210.

In some embodiments, a ring based interconnect unit 212 is used tocouple the internal components of the processor 200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 212 via an I/O link213.

The exemplary I/O link 213 represents at least one of multiple varietiesof I/O interconnects, including an on package I/O interconnect whichfacilitates communication between various processor components and ahigh-performance embedded memory module 218, such as an eDRAM module. Insome embodiments, each of the processor cores 202-202N and graphicsprocessor 208 use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 202A-202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 202A-202N are heterogeneous in terms of instruction setarchitecture (ISA), where one or more of processor cores 202A-N executea first instruction set, while at least one of the other cores executesa subset of the first instruction set or a different instruction set. Inone embodiment processor cores 202A-202N are heterogeneous in terms ofmicroarchitecture, where one or more cores having a relatively higherpower consumption couple with one or more power cores having a lowerpower consumption. Additionally, processor 200 can be implemented on oneor more chips or as an SoC integrated circuit having the illustratedcomponents, in addition to other components.

FIG. 8 is a block diagram of a graphics processor 300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 300 includes amemory interface 314 to access memory. Memory interface 314 can be aninterface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 300 also includes a displaycontroller 302 to drive display output data to a display device 320.Display controller 302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 300 includesa video codec engine 306 to encode, decode, or transcode media to, from,or between one or more media encoding formats, including, but notlimited to Moving Picture Experts Group (MPEG) formats such as MPEG-2,Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, as well asthe Society of Motion Picture & Television Engineers (SMPTE) 421M/VC-1,and Joint Photographic Experts Group (JPEG) formats such as JPEG, andMotion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block imagetransfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, graphics processing engine 310 is a compute engine forperforming graphics operations, including three-dimensional (3D)graphics operations and media operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 312 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 306. In some embodiments, media pipeline 316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executingthreads spawned by 3D pipeline 312 and media pipeline 316. In oneembodiment, the pipelines send thread execution requests to 3D/Mediasubsystem 315, which includes thread dispatch logic for arbitrating anddispatching the various requests to available thread executionresources. The execution resources include an array of graphicsexecution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 315 includes one or more internal cachesfor thread instructions and data. In some embodiments, the subsystemalso includes shared memory, including registers and addressable memory,to share data between threads and to store output data.

3D/Media Processing

FIG. 9 is a block diagram of a graphics processing engine 410 of agraphics processor in accordance with some embodiments. In oneembodiment, the GPE 410 is a version of the GPE 310 shown in FIG. 8.Elements of FIG. 9 having the same reference numbers (or names) as theelements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, GPE 410 couples with a command streamer 403, whichprovides a command stream to the GPE 3D and media pipelines 412, 416. Insome embodiments, command streamer 403 is coupled to memory, which canbe system memory, or one or more of internal cache memory and sharedcache memory. In some embodiments, command streamer 403 receivescommands from the memory and sends the commands to 3D pipeline 412and/or media pipeline 416. The commands are directives fetched from aring buffer, which stores commands for the 3D and media pipelines 412,416. In one embodiment, the ring buffer can additionally include batchcommand buffers storing batches of multiple commands. The 3D and mediapipelines 412, 416 process the commands by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to an execution unit array 414. In some embodiments,execution unit array 414 is scalable, such that the array includes avariable number of execution units based on the target power andperformance level of GPE 410.

In some embodiments, a sampling engine 430 couples with memory (e.g.,cache memory or system memory) and execution unit array 414. In someembodiments, sampling engine 430 provides a memory access mechanism forexecution unit array 414 that allows execution array 414 to readgraphics and media data from memory. In some embodiments, samplingengine 430 includes logic to perform specialized image samplingoperations for media.

In some embodiments, the specialized media sampling logic in samplingengine 430 includes a de-noise/de-interlace module 432, a motionestimation module 434, and an image scaling and filtering module 436. Insome embodiments, de-noise/de-interlace module 432 includes logic toperform one or more of a de-noise or a de-interlace algorithm on decodedvideo data. The de-interlace logic combines alternating fields ofinterlaced video content into a single fame of video. The de-noise logicreduces or removes data noise from video and image data. In someembodiments, the de-noise logic and de-interlace logic are motionadaptive and use spatial or temporal filtering based on the amount ofmotion detected in the video data. In some embodiments, thede-noise/de-interlace module 432 includes dedicated motion detectionlogic (e.g., within the motion estimation engine 434).

In some embodiments, motion estimation engine 434 provides hardwareacceleration for video operations by performing video accelerationfunctions such as motion vector estimation and prediction on video data.The motion estimation engine determines motion vectors that describe thetransformation of image data between successive video frames. In someembodiments, a graphics processor media codec uses video motionestimation engine 434 to perform operations on video at the macro-blocklevel that may otherwise be too computationally intensive to performwith a general-purpose processor. In some embodiments, motion estimationengine 434 is generally available to graphics processor components toassist with video decode and processing functions that are sensitive oradaptive to the direction or magnitude of the motion within video data.

In some embodiments, image scaling and filtering module 436 performsimage-processing operations to enhance the visual quality of generatedimages and video. In some embodiments, scaling and filtering module 436processes image and video data during the sampling operation beforeproviding the data to execution unit array 414.

In some embodiments, the GPE 410 includes a data port 444, whichprovides an additional mechanism for graphics subsystems to accessmemory. In some embodiments, data port 444 facilitates memory access foroperations including render target writes, constant buffer reads,scratch memory space reads/writes, and media surface accesses. In someembodiments, data port 444 includes cache memory space to cache accessesto memory. The cache memory can be a single data cache or separated intomultiple caches for the multiple subsystems that access memory via thedata port (e.g., a render buffer cache, a constant buffer cache, etc.).In some embodiments, threads executing on an execution unit in executionunit array 414 communicate with the data port by exchanging messages viaa data distribution interconnect that couples each of the sub-systems ofGPE 410.

Execution Units

FIG. 10 is a block diagram of another embodiment of a graphics processor500. Elements of FIG. 10 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 500 includes a ring interconnect502, a pipeline front-end 504, a media engine 537, and graphics cores580A-580N. In some embodiments, ring interconnect 502 couples thegraphics processor to other processing units, including other graphicsprocessors or one or more general-purpose processor cores. In someembodiments, the graphics processor is one of many processors integratedwithin a multi-core processing system.

In some embodiments, graphics processor 500 receives batches of commandsvia ring interconnect 502. The incoming commands are interpreted by acommand streamer 503 in the pipeline front-end 504. In some embodiments,graphics processor 500 includes scalable execution logic to perform 3Dgeometry processing and media processing via the graphics core(s)580A-580N. For 3D geometry processing commands, command streamer 503supplies commands to geometry pipeline 536. For at least some mediaprocessing commands, command streamer 503 supplies the commands to avideo front end 534, which couples with a media engine 537. In someembodiments, media engine 537 includes a Video Quality Engine (VQE) 530for video and image post-processing and a multi-format encode/decode(MFX) 533 engine to provide hardware-accelerated media data encode anddecode. In some embodiments, geometry pipeline 536 and media engine 537each generate execution threads for the thread execution resourcesprovided by at least one graphics core 580A.

In some embodiments, graphics processor 500 includes scalable threadexecution resources featuring modular cores 580A-580N (sometimesreferred to as core slices), each having multiple sub-cores 550A-550N,560A-560N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 500 can have any number of graphicscores 580A through 580N. In some embodiments, graphics processor 500includes a graphics core 580A having at least a first sub-core 550A anda second core sub-core 560A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 550A).In some embodiments, graphics processor 500 includes multiple graphicscores 580A-580N, each including a set of first sub-cores 550A-550N and aset of second sub-cores 560A-560N. Each sub-core in the set of firstsub-cores 550A-550N includes at least a first set of execution units552A-552N and media/texture samplers 554A-554N. Each sub-core in the setof second sub-cores 560A-560N includes at least a second set ofexecution units 562A-562N and samplers 564A-564N. In some embodiments,each sub-core 550A-550N, 560A-560N shares a set of shared resources570A-570N. In some embodiments, the shared resources include sharedcache memory and pixel operation logic. Other shared resources may alsobe included in the various embodiments of the graphics processor.

FIG. 11 illustrates thread execution logic 600 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 11 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 600 includes a pixel shader602, a thread dispatcher 604, instruction cache 606, a scalableexecution unit array including a plurality of execution units 608A-608N,a sampler 610, a data cache 612, and a data port 614. In one embodimentthe included components are interconnected via an interconnect fabricthat links to each of the components. In some embodiments, threadexecution logic 600 includes one or more connections to memory, such assystem memory or cache memory, through one or more of instruction cache606, data port 614, sampler 610, and execution unit array 608A-608N. Insome embodiments, each execution unit (e.g. 608A) is an individualvector processor capable of executing multiple simultaneous threads andprocessing multiple data elements in parallel for each thread. In someembodiments, execution unit array 608A-608N includes any numberindividual execution units.

In some embodiments, execution unit array 608A-608N is primarily used toexecute “shader” programs. In some embodiments, the execution units inarray 608A-608N execute an instruction set that includes native supportfor many standard 3D graphics shader instructions, such that shaderprograms from graphics libraries (e.g., Direct 3D and OpenGL) areexecuted with a minimal translation. The execution units support vertexand geometry processing (e.g., vertex programs, geometry programs,vertex shaders), pixel processing (e.g., pixel shaders, fragmentshaders) and general-purpose processing (e.g., compute and mediashaders).

Each execution unit in execution unit array 608A-608N operates on arraysof data elements. The number of data elements is the “execution size,”or the number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 608A-608N support integer andfloating-point data types.

The execution unit instruction set includes single instruction multipledata (SIMD) instructions. The various data elements can be stored as apacked data type in a register and the execution unit will process thevarious elements based on the data size of the elements. For example,when operating on a 256-bit wide vector, the 256 bits of the vector arestored in a register and the execution unit operates on the vector asfour separate 64-bit packed data elements (Quad-Word (QW) size dataelements), eight separate 32-bit packed data elements (Double Word (DW)size data elements), sixteen separate 16-bit packed data elements (Word(W) size data elements), or thirty-two separate 8-bit data elements(byte (B) size data elements). However, different vector widths andregister sizes are possible.

One or more internal instruction caches (e.g., 606) are included in thethread execution logic 600 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,612) are included to cache thread data during thread execution. In someembodiments, sampler 610 is included to provide texture sampling for 3Doperations and media sampling for media operations. In some embodiments,sampler 610 includes specialized texture or media sampling functionalityto process texture or media data during the sampling process beforeproviding the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 600 via thread spawningand dispatch logic. In some embodiments, thread execution logic 600includes a local thread dispatcher 604 that arbitrates thread initiationrequests from the graphics and media pipelines and instantiates therequested threads on one or more execution units 608A-608N. For example,the geometry pipeline (e.g., 536 of FIG. 10) dispatches vertexprocessing, tessellation, or geometry processing threads to threadexecution logic 600 (FIG. 11). In some embodiments, thread dispatcher604 can also process runtime thread spawning requests from the executingshader programs.

Once a group of geometric objects has been processed and rasterized intopixel data, pixel shader 602 is invoked to further compute outputinformation and cause results to be written to output surfaces (e.g.,color buffers, depth buffers, stencil buffers, etc.). In someembodiments, pixel shader 602 calculates the values of the variousvertex attributes that are to be interpolated across the rasterizedobject. In some embodiments, pixel shader 602 then executes anapplication programming interface (API)-supplied pixel shader program.To execute the pixel shader program, pixel shader 602 dispatches threadsto an execution unit (e.g., 608A) via thread dispatcher 604. In someembodiments, pixel shader 602 uses texture sampling logic in sampler 610to access texture data in texture maps stored in memory. Arithmeticoperations on the texture data and the input geometry data compute pixelcolor data for each geometric fragment, or discards one or more pixelsfrom further processing.

In some embodiments, the data port 614 provides a memory accessmechanism for the thread execution logic 600 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 614 includes or couples to one or more cachememories (e.g., data cache 612) to cache data for memory access via thedata port.

FIG. 12 is a block diagram illustrating a graphics processor instructionformats 700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 700 described and illustrated are macro-instructions,in that they are instructions supplied to the execution unit, as opposedto micro-operations resulting from instruction decode once theinstruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit format 710. A 64-bit compactedinstruction format 730 is available for some instructions based on theselected instruction, instruction options, and number of operands. Thenative 128-bit format 710 provides access to all instruction options,while some options and operations are restricted in the 64-bit format730. The native instructions available in the 64-bit format 730 vary byembodiment. In some embodiments, the instruction is compacted in partusing a set of index values in an index field 713. The execution unithardware references a set of compaction tables based on the index valuesand uses the compaction table outputs to reconstruct a nativeinstruction in the 128-bit format 710.

For each format, instruction opcode 712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). For 128-bitinstructions 710 an exec-size field 716 limits the number of datachannels that will be executed in parallel. In some embodiments,exec-size field 716 is not available for use in the 64-bit compactinstruction format 730.

Some execution unit instructions have up to three operands including twosource operands, src0 722, src1 722, and one destination 718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 724), where the instructionopcode 712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode information 726 specifying, for example, whetherdirect register addressing mode or indirect register addressing mode isused. When direct register addressing mode is used, the register addressof one or more operands is directly provided by bits in the instruction710.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726, which specifies an address mode and/or anaccess mode for the instruction. In one embodiment the access mode todefine a data access alignment for the instruction. Some embodimentssupport access modes including a 16-byte aligned access mode and a1-byte aligned access mode, where the byte alignment of the access modedetermines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction 710 may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction 710 may use 16-byte-aligned addressing for allsource and destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction 710 directly provide the register address of one ormore operands. When indirect register addressing mode is used, theregister address of one or more operands may be computed based on anaddress register value and an address immediate field in theinstruction.

In some embodiments instructions are grouped based on opcode 712bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes amix of instructions, including synchronization instructions (e.g., wait,send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instructiongroup 748 includes component-wise arithmetic instructions (e.g., add,multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel mathgroup 748 performs the arithmetic operations in parallel across datachannels. The vector math group 750 includes arithmetic instructions(e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math groupperforms arithmetic such as dot product calculations on vector operands.

Graphics Pipeline

FIG. 13 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 13 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 800 includes a graphics pipeline820, a media pipeline 830, a display engine 840, thread execution logic850, and a render output pipeline 870. In some embodiments, graphicsprocessor 800 is a graphics processor within a multi-core processingsystem that includes one or more general purpose processing cores. Thegraphics processor is controlled by register writes to one or morecontrol registers (not shown) or via commands issued to graphicsprocessor 800 via a ring interconnect 802. In some embodiments, ringinterconnect 802 couples graphics processor 800 to other processingcomponents, such as other graphics processors or general-purposeprocessors. Commands from ring interconnect 802 are interpreted by acommand streamer 803, which supplies instructions to individualcomponents of graphics pipeline 820 or media pipeline 830.

In some embodiments, command streamer 803 directs the operation of avertex fetcher 805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 803. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 807 execute vertex-processing instructions by dispatchingexecution threads to execution units 852A, 852B via a thread dispatcher831.

In some embodiments, execution units 852A, 852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 852A, 852B have anattached L1 cache 851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 813 operatesat the direction of hull shader 811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 820. Insome embodiments, if tessellation is not used, tessellation components811, 813, 817 can be bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 819 via one or more threads dispatched to executionunits 852A, 852B, or can proceed directly to the clipper 829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader819 receives input from the vertex shader 807. In some embodiments,geometry shader 819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 processes vertex data. The clipper829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 873 in the render output pipeline870 dispatches pixel shaders to convert the geometric objects into theirper pixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 850. In some embodiments, anapplication can bypass the rasterizer 873 and access un-rasterizedvertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric,or some other interconnect mechanism that allows data and messagepassing amongst the major components of the processor. In someembodiments, execution units 852A, 852B and associated cache(s) 851,texture and media sampler 854, and texture/sampler cache 858interconnect via a data port 856 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 854, caches 851, 858 and execution units 852A,852B each have separate memory access paths.

In some embodiments, render output pipeline 870 contains a rasterizerand depth test component 873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache 878and depth cache 879 are also available in some embodiments. A pixeloperations component 877 performs pixel-based operations on the data,though in some instances, pixel operations associated with 2D operations(e.g. bit block image transfers with blending) are performed by the 2Dengine 841, or substituted at display time by the display controller 843using overlay display planes. In some embodiments, a shared L3 cache 875is available to all graphics components, allowing the sharing of datawithout the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes amedia engine 837 and a video front end 834. In some embodiments, videofront end 834 receives pipeline commands from the command streamer 803.In some embodiments, media pipeline 830 includes a separate commandstreamer. In some embodiments, video front-end 834 processes mediacommands before sending the command to the media engine 837. In someembodiments, media engine 337 includes thread spawning functionality tospawn threads for dispatch to thread execution logic 850 via threaddispatcher 831.

In some embodiments, graphics processor 800 includes a display engine840. In some embodiments, display engine 840 is external to processor800 and couples with the graphics processor via the ring interconnect802, or some other interconnect bus or fabric. In some embodiments,display engine 840 includes a 2D engine 841 and a display controller843. In some embodiments, display engine 840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 820 and media pipeline 830 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL) and Open Computing Language (OpenCL)from the Khronos Group, the Direct3D library from the MicrosoftCorporation, or support may be provided to both OpenGL and D3D. Supportmay also be provided for the Open Source Computer Vision Library(OpenCV). A future API with a compatible 3D pipeline would also besupported if a mapping can be made from the pipeline of the future APIto the pipeline of the graphics processor.

Graphics Pipeline Programming

FIG. 14A is a block diagram illustrating a graphics processor commandformat 900 according to some embodiments. FIG. 14B is a block diagramillustrating a graphics processor command sequence 910 according to anembodiment. The solid lined boxes in FIG. 14A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 900 of FIG. 14A includes data fields to identify a targetclient 902 of the command, a command operation code (opcode) 904, andthe relevant data 906 for the command. A sub-opcode 905 and a commandsize 908 are also included in some commands.

In some embodiments, client 902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 904 and, if present, sub-opcode 905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 906. For some commands an explicit commandsize 908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 14B shows an exemplary graphics processorcommand sequence 910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 922 and the media pipeline 924 do notoperate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 913 isrequired only once within an execution context before issuing pipelinecommands unless the context is to issue commands for both pipelines. Insome embodiments, a pipeline flush command is 912 is requiredimmediately before a pipeline switch via the pipeline select command913.

In some embodiments, a pipeline control command 914 configures agraphics pipeline for operation and is used to program the 3D pipeline922 and the media pipeline 924. In some embodiments, pipeline controlcommand 914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 916 includes selecting the size and number of returnbuffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 920,the command sequence is tailored to the 3D pipeline 922 beginning withthe 3D pipeline state 930, or the media pipeline 924 beginning at themedia pipeline state 940.

The commands for the 3D pipeline state 930 include 3D state settingcommands for vertex buffer state, vertex element state, constant colorstate, depth buffer state, and other state variables that are to beconfigured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based the particular 3DAPI in use. In some embodiments, 3D pipeline state 930 commands are alsoable to selectively disable or bypass certain pipeline elements if thoseelements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 922 dispatches shader execution threads to graphicsprocessor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 910 followsthe media pipeline 924 path when performing media operations. Ingeneral, the specific use and manner of programming for the mediapipeline 924 depends on the media or compute operations to be performed.Specific media decode operations may be offloaded to the media pipelineduring media decode. In some embodiments, the media pipeline can also bebypassed and media decode can be performed in whole or in part usingresources provided by one or more general purpose processing cores. Inone embodiment, the media pipeline also includes elements forgeneral-purpose graphics processor unit (GPGPU) operations, where thegraphics processor is used to perform SIMD vector operations usingcomputational shader programs that are not explicitly related to therendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similarmanner as the 3D pipeline 922. A set of media pipeline state commands940 are dispatched or placed into in a command queue before the mediaobject commands 942. In some embodiments, media pipeline state commands940 include data to configure the media pipeline elements that will beused to process the media objects. This includes data to configure thevideo decode and video encode logic within the media pipeline, such asencode or decode format. In some embodiments, media pipeline statecommands 940 also support the use one or more pointers to “indirect”state elements that contain a batch of state settings.

In some embodiments, media object commands 942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 942. Once the pipeline state is configured andmedia object commands 942 are queued, the media pipeline 924 istriggered via an execute command 944 or an equivalent execute event(e.g., register write). Output from media pipeline 924 may then be postprocessed by operations provided by the 3D pipeline 922 or the mediapipeline 924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 15 illustrates exemplary graphics software architecture for a dataprocessing system 1000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1010, an operating system 1020, and at least one processor 1030. In someembodiments, processor 1030 includes a graphics processor 1032 and oneor more general-purpose processor core(s) 1034. The graphics application1010 and operating system 1020 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 1010 contains one or moreshader programs including shader instructions 1012. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 1014 in a machinelanguage suitable for execution by the general-purpose processor core1034. The application also includes graphics objects 1016 defined byvertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. When the Direct3D API is in use, theoperating system 1020 uses a front-end shader compiler 1024 to compileany shader instructions 1012 in HLSL into a lower-level shader language.The compilation may be a just-in-time (JIT) compilation or theapplication can perform shader pre-compilation. In some embodiments,high-level shaders are compiled into low-level shaders during thecompilation of the 3D graphics application 1010.

In some embodiments, user mode graphics driver 1026 contains a back-endshader compiler 1027 to convert the shader instructions 1012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1012 in the GLSL high-level language are passed to a usermode graphics driver 1026 for compilation. In some embodiments, usermode graphics driver 1026 uses operating system kernel mode functions1028 to communicate with a kernel mode graphics driver 1029. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 16 is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility1130 can generate a software simulation 1110 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation1110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 1112. The simulation model 1112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design can then be created or synthesized from thesimulation model 11001112. The RTL design 1115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 1115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by thedesign facility into a hardware model 1120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3rdparty fabrication facility 1165 using non-volatile memory 1140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 1150 or wireless connection 1160. Thefabrication facility 1165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 17 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1200 that may be fabricated using one or more IPcores, according to an embodiment. The exemplary integrated circuitincludes one or more application processors 1205 (e.g., CPUs), at leastone graphics processor 1210, and may additionally include an imageprocessor 1215 and/or a video processor 1220, any of which may be amodular IP core from the same or multiple different design facilities.The integrated circuit includes peripheral or bus logic including a USBcontroller 1225, UART controller 1230, an SPI/SDIO controller 1235, andan I2S/I2C controller 1240. Additionally, the integrated circuit caninclude a display device 1245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 1255. Storage maybe provided by a flash memory subsystem 1260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine1270.

Additionally, other logic and circuits may be included in the processorof integrated circuit 1200, including additional graphicsprocessors/cores, peripheral interface controllers, or general purposeprocessor cores.

Additional Notes and Examples

Example 1 may include a system to segment an image using mean shifting,comprising a camera to capture an image that comprises pixels havingpixel values, a histogram generator to generate a histogram of the pixelvalues and to divide the histogram into two or more class intervals,wherein each class interval has boundaries and a width, a meandeterminer to compute an initial mean and a weighted mean for each classinterval, a class interval mean updater to update a mean of each classinterval to a most recently computed weighted mean if a magnitude of adifference between the weighted mean and a previous mean is greater thana limit, a class interval boundary updater to update the boundaries ofeach class interval based on a respective width and a respective mostrecently computed mean for each class interval if the magnitude of thedifference is greater than the limit, a pixel value updater to updatethe pixel values of each class interval to a last computed weighted meanfor a respective class interval if the magnitude of the difference isnot greater than the limit, and a display to display a segmented imagebased on the updated pixel values.

Example 2 may include the system of Example 1, wherein each pixel valueis to correspond to one of a brightness, a color, an intensity, or adistance.

Example 3 may include the system of any one of Examples 1 to 2, whereinthe system is to apply the segmented image in one or more of objectrecognition, surveillance, or tracking.

Example 4 may include the system of any one of Examples 1 to 3, whereineach class interval is to have a same width.

Example 5 may include the system of any one of Examples 1 to 4, whereinthe initial mean of a class interval is to be set to a midpoint of theclass interval.

Example 6 may include the system of any one of Examples 1 to 5, whereineach class interval is to include at least one bin and the weighted meanfor each class interval is to be computed using a weighting function ofthe number of pixels in the at least one bin.

Example 7 may include the system of any one of Examples 1 to 6, whereina number of segments in the segmented image is equal to a number ofclass intervals.

Example 8 may include an apparatus to segment an image using meanshifting, comprising a pixel value determiner to determine a pixel valuefor pixels in an image, a histogram generator to generate a histogram ofthe pixel values and to divide the histogram into two or more classintervals, wherein each class interval has boundaries and a width, amean determiner to compute an initial mean and a weighted mean for eachclass interval, a class interval mean updater to update a mean of eachclass interval to a most recently computed weighted mean if a magnitudeof a difference between the weighted mean and a previous mean is greaterthan a limit, a class interval boundary updater to update the boundariesof each class interval based on a respective width and a respective mostrecently computed mean for each class interval if the magnitude of thedifference is greater than the limit, and a pixel value updater toupdate the pixel values of each class interval to a last computedweighted mean for a respective class interval if the magnitude of thedifference is not greater than the limit.

Example 9 may include the apparatus of Example 8, wherein each pixelvalue is to correspond to one of a brightness, a color, an intensity, ora distance.

Example 10 may include the apparatus of any one of Examples 8 to 9,wherein the apparatus is to apply the segmented image in one or more ofobject recognition, surveillance, or tracking.

Example 11 may include the apparatus of any one of Examples 8 to 10,wherein each class interval is to have a same width.

Example 12 may include the apparatus of any one of Examples 8 to 11,wherein the initial mean of a class interval is to be set to a midpointof the class interval.

Example 13 may include the apparatus of any one of Examples 8 to 12,wherein each class interval is to include at least one bin and theweighted mean of each class interval is to be computed using a weightingfunction of the number of pixels in the at least one bin.

Example 14 may include the apparatus of any one of Examples 8 to 13,wherein a number of segments in the segmented image is to be equal to anumber of class intervals.

Example 15 may include a method to segment a data set, comprisinggenerating a histogram of data values, dividing the histogram into aplurality of class intervals, wherein each class interval has an upperboundary, a lower boundary, and a width, computing an initial mean and aweighted mean for each class interval, iteratively re-computing theweighted mean of each class interval and updating the boundaries of eachclass interval until a difference between the weighted mean and aprevious mean is not greater than a limit, wherein the data values ofeach class interval are updated to a last computed weighted mean for arespective class interval, and constructing a segmented data set fromthe updated data values.

Example 16 may include the method of Example 15, wherein each data valueis associated with a pixel and is to correspond to one of a brightness,a color, an intensity, or a distance.

Example 17 may include the method of any one of Examples 15 to 16,further including applying the segmented data to one or more of objectrecognition, surveillance, or tracking.

Example 18 may include the method of any one of Examples 15 to 17,wherein each class interval has the same width.

Example 19 may include the method of any one of Examples 15 to 18,wherein the initial mean of a class interval is set to a midpoint of theclass interval.

Example 20 may include the method of any one of Examples 15 to 19,wherein each class interval includes at least one bin and the weightedmean of each class interval is computed using a weighting function ofthe number of pixels in the at least one bin.

Example 21 may include the at least one computer readable storage mediumcomprising a set of instructions which, when executed by a computingdevice, cause the computing device to generate a histogram of pixelvalues, divide the histogram into two or more class intervals eachhaving boundaries and a width, compute an initial mean and a weightedmean for each class interval, update a mean of each class interval to amost recently computed weighted mean if a magnitude of a differencebetween the weighted mean and a previous mean is greater than a limit,update the boundaries of each class interval based on a respective widthand a respective most recently computed mean for the class interval ifthe magnitude of the difference is greater than the limit, update thepixel values of each class interval to a last computed weighted mean fora respective class interval if the magnitude of the difference is notgreater than the limit, and construct a segmented image from the updatedpixels.

Example 22 may include the at least one computer readable storage mediumof Example 21, wherein each pixel value is to correspond to one of abrightness, a color, an intensity, or a distance.

Example 23 may include at least one computer readable storage medium ofany one of Examples 21 to 22, wherein the computing device is to applythe segmented image in one or more of object recognition, surveillance,or tracking.

Example 24 may include at least one computer readable storage medium ofany one of Examples 21 to 23, wherein each class interval is to have asame width.

Example 25 may include at least one computer readable storage medium ofany one of Examples 21 to 24, wherein the instructions, when executed,cause a computing device to set the initial mean of a class interval toa midpoint of the class interval, and compute the weighted mean using aweighting function based on a number of pixels in a bin in the classinterval.

Example 26 may include an apparatus for segmenting an image comprisingmeans for determining a pixel value for each pixel in an image, meansfor generating a histogram of the pixel values and dividing thehistogram into two or more class intervals, wherein each class intervalhas boundaries and a width, means for computing an initial mean and aweighted mean for each of the class intervals, means for updating a meanof each class interval to a most recently computed weighted mean if themagnitude |D| of a difference between the weighted mean and a previousmean is greater than a limit L, means for updating the boundaries ofeach class interval based on a respective width and a respective mostrecently computed mean for a respective class interval if |D|>L, andmeans for updating the pixel values of each class interval to a lastcomputed weighted mean for a respective class interval if |D|≦L.

Example 27 may include the apparatus of Example 26, wherein each pixelvalue is to correspond to one of a brightness, a color, an intensity, ora distance.

Example 28 may include the apparatus of any one of Examples 26 to 27,further including means for applying the segmented image in one or moreof object recognition, surveillance, or tracking.

Example 29 may include the apparatus of any one of Examples 26 to 28,wherein each class interval is to have a same width.

Example 30 may include the apparatus of any one of Examples 26 to 29,wherein the initial mean of a class interval is to be set to a midpointof the class interval.

Example 31 may include the apparatus of any one of Examples 26 to 30,wherein each class interval includes at least one bin and the weightedmean is to be computed using a weighting function of a number of pixelsin the at least one bin.

Example 32 may include the apparatus of any one of Examples 26 to 31,wherein the number of segments in the segmented image is to be equal toa number of class intervals.

Example 33 may include a method to segment an image using mean shifting,comprising generating a histogram of pixel values, dividing thehistogram into two or more class intervals each having boundaries and awidth, computing an initial mean and a weighted mean for each of theclass intervals, updating a mean of each class interval to a mostrecently computed weighted mean if a magnitude of a difference betweenthe weighted mean and a previous mean is greater than a limit, updatingthe boundaries of each class interval based on a respective width and arespective most recently computed mean for each class interval if themagnitude of the difference is greater than the limit, updating thepixel values of each class interval to a last computed weighted mean fora respective class interval if the magnitude of the difference is notgreater than the limit; and constructing a segmented image from theupdated pixels, wherein each segment corresponds to a class interval.

Example 34 may include the method of Example 33, wherein each of theclass intervals has a midpoint, and the initial mean of each classinterval is the midpoint.

Example 35 may include the method of any one of Examples 33 to 34,wherein the boundaries of each class interval are updated to a weightedmean plus-or-minus half the width of the class interval.

Example 36 may include the method of any one of Examples 33 to 35,wherein each class interval is divided into bins.

Example 37 may include the method of any one of Examples 33 to 36,wherein each bin includes one or more pixel values.

Example 38 may include the method of any one of Examples 33 to 37,wherein each bin includes only one pixel value.

Example 39 may include the method of any one of Examples 33 to 38,wherein the weighted mean of each class interval is computed using aweighting function of a number of pixels in the at least one bin.

Example 40 may include the method of any one of Examples 33 to 39,wherein the weighting function is the number of pixels a bin.

Example 41 may include the method of any one of Examples 33 to 40,wherein a number of image segments corresponds to a number of classintervals.

Embodiments are applicable for use with all types of semiconductorintegrated circuit (“IC”) chips. Examples of these IC chips include butare not limited to processors, controllers, chipset components,programmable logic arrays (PLAs), memory chips, network chips, systemson chip (SoCs), SSD/NAND controller ASICs, and the like. In addition, insome of the drawings, signal conductor lines are represented with lines.Some may be different, to indicate more constituent signal paths, have anumber label, to indicate a number of constituent signal paths, and/orhave arrows at one or more ends, to indicate primary information flowdirection. This, however, should not be construed in a limiting manner.Rather, such added detail may be used in connection with one or moreexemplary embodiments to facilitate easier understanding of a circuit.Any represented signal lines, whether or not having additionalinformation, may actually comprise one or more signals that may travelin multiple directions and may be implemented with any suitable type ofsignal scheme, e.g., digital or analog lines implemented withdifferential pairs, optical fiber lines, and/or single-ended lines.

Example sizes/models/values/ranges may have been given, althoughembodiments are not limited to the same. As manufacturing techniques(e.g., photolithography) mature over time, it is expected that devicesof smaller size could be manufactured. In addition, well knownpower/ground connections to IC chips and other components may or may notbe shown within the figures, for simplicity of illustration anddiscussion, and so as not to obscure certain aspects of the embodiments.Further, arrangements may be shown in block diagram form in order toavoid obscuring embodiments, and also in view of the fact that specificswith respect to implementation of such block diagram arrangements arehighly dependent upon the platform within which the embodiment is to beimplemented, i.e., such specifics should be well within purview of oneskilled in the art. Where specific details (e.g., circuits) are setforth in order to describe example embodiments, it should be apparent toone skilled in the art that embodiments can be practiced without, orwith variation of, these specific details. The description is thus to beregarded as illustrative instead of limiting.

The term “coupled” may be used herein to refer to any type ofrelationship, direct or indirect, between the components in question,and may apply to electrical, mechanical, fluid, optical,electromagnetic, electromechanical or other connections. In addition,the terms “first”, “second”, etc. may be used herein only to facilitatediscussion, and carry no particular temporal or chronologicalsignificance unless otherwise indicated.

As used in this application and in the claims, a list of items joined bythe term “one or more of” or “at least one of” may mean any combinationof the listed terms. For example, the phrases “one or more of A, B or C”may mean A; B; C; A and B; A and C; B and C; or A, B and C. In addition,a list of items joined by the term “and so forth” or “etc.” may mean anycombination of the listed terms as well any combination with otherterms.

Those skilled in the art will appreciate from the foregoing descriptionthat the broad techniques of the embodiments can be implemented in avariety of forms. Therefore, while the embodiments have been describedin connection with particular examples thereof, the true scope of theembodiments should not be so limited since other modifications willbecome apparent to the skilled practitioner upon a study of thedrawings, specification, and following claims.

We claim:
 1. A system to segment an image using mean shifting,comprising: a camera to capture an image that comprises pixels havingpixel values; a processor to control: a histogram generator to generatea histogram of the pixel values and to divide the histogram into two ormore class intervals, wherein each class interval has boundaries and awidth; a mean determiner to compute an initial mean and subsequently tocompute a weighted mean for each class interval; a class interval meanupdater to iteratively update the weighted mean of each class intervalto a most recently computed weighted mean if a magnitude of a differencebetween the weighted mean and a previous mean is greater than a limit,wherein the previous mean is the initial mean when the initial mean hasmost recently been computed or a previously computed weighted mean whenthe previously computed weighted mean has most recently been computed; aclass interval boundary updater to update the boundaries of each classinterval based on a respective width and a respective most recentlycomputed weighted mean for each class interval if the magnitude of thedifference is greater than the limit; and a pixel value updater toupdate the pixel values of each class interval to a last computedweighted mean for a respective class interval if the magnitude of thedifference is not greater than the limit; and a display to display asegmented image based on the updated pixel values.
 2. The system ofclaim 1, wherein each pixel value is to correspond to one of abrightness, a color, an intensity, or a distance.
 3. The system of claim1, wherein the system is to apply the segmented image in one or more ofobject recognition, surveillance, or tracking.
 4. The system of claim 1,wherein each class interval is to have a same width.
 5. The system ofclaim 1, wherein the initial mean of a class interval is to be set to amidpoint of the class interval.
 6. The system of claim 5, wherein eachclass interval is to include at least one bin and the weighted mean foreach class interval is to be computed using a weighting function of thenumber of pixels in the at least one bin.
 7. The system of claim 1,wherein a number of segments in the segmented image is equal to a numberof class intervals.
 8. An apparatus to segment an image using meanshifting, comprising: a processor to control: a pixel value determinerto determine a pixel value for pixels in an image; a histogram generatorto generate a histogram of the pixel values and to divide the histograminto two or more class intervals, wherein each class interval hasboundaries and a width; a mean determiner to compute an initial mean andsubsequently to compute a weighted mean for each class interval; a classinterval mean updater to iteratively update the weighted mean of eachclass interval to a most recently computed weighted mean if a magnitudeof a difference between the weighted mean and a previous mean is greaterthan a limit, wherein the previous mean is the initial mean when theinitial mean has most recently been computed or a previously computedweighted mean when the previously computed weighted mean has mostrecently been computed; a class interval boundary updater to update theboundaries of each class interval based on a respective width and arespective most recently computed weighted mean for each class intervalif the magnitude of the difference is greater than the limit; and apixel value updater to update the pixel values of each class interval toa last computed weighted mean for a respective class interval if themagnitude of the difference is not greater than the limit.
 9. Theapparatus of claim 8, wherein each pixel value is to correspond to oneof a brightness, a color, an intensity, or a distance.
 10. The apparatusof claim 8, wherein the apparatus is to apply the segmented image in oneor more of object recognition, surveillance, or tracking.
 11. Theapparatus of claim 8, wherein each class interval is to have a samewidth.
 12. The apparatus of claim 8, wherein the initial mean of a classinterval is to be set to a midpoint of the class interval.
 13. Theapparatus of claim 12, wherein each class interval is to include atleast one bin and the weighted mean of each class interval is to becomputed using a weighting function of the number of pixels in the atleast one bin.
 14. The apparatus of claim 8, wherein a number ofsegments in the segmented image is to be equal to a number of classintervals.
 15. A method to segment a data set, comprising: generating ahistogram of data values; dividing the histogram into a plurality ofclass intervals, wherein each class interval has an upper boundary, alower boundary, and a width; computing an initial mean and subsequentlycomputing a weighted mean for each class interval; iterativelyre-computing the weighted mean of each class interval and updating theboundaries of each class interval until a difference between theweighted mean and a previous mean is not greater than a limit, whereinthe previous mean is the initial mean when the initial mean has mostrecently been computed or a previously computed weighted mean when thepreviously computed weighted mean has most recently been computed, andwherein the data values of each class interval are updated to a lastcomputed weighted mean for a respective class interval; and constructinga segmented data set from the updated data values.
 16. The method ofclaim 15, wherein each data value is associated with a pixel in an imageand is to correspond to one of a brightness, a color, an intensity, or adistance.
 17. The method of claim 15, further including applying thesegmented data set to one or more of object recognition, surveillance,or tracking.
 18. The method of claim 15, wherein each class interval hasa same width.
 19. The method of claim 15, wherein the initial mean of aclass interval is set to a midpoint of the class interval.
 20. Themethod of claim 15, wherein each class interval includes at least onebin and the weighted mean of each class interval is computed using aweighting function of a number of pixels in the at least one bin.
 21. Atleast one non-transitory computer readable storage medium comprising aset of instructions which, when executed by a computing device, causethe computing device to: generate a histogram of pixel values; dividethe histogram into two or more class intervals each having boundariesand a width; compute an initial mean and subsequently compute a weightedmean for each class interval; iteratively update the weighted mean ofeach class interval to a most recently computed weighted mean if amagnitude of a difference between the weighted mean and a previous meanis greater than a limit, wherein the previous mean is the initial meanwhen the initial mean has most recently been computed or a previouslycomputed weighted mean when the previously computed weighted mean hasmost recently been computed; update the boundaries of each classinterval based on a respective width and a respective most recentlycomputed mean for the class interval if the magnitude of the differenceis greater than the limit; update the pixel values of each classinterval to a last computed weighted mean for a respective classinterval if the magnitude of the difference is not greater than thelimit; and construct a segmented image from the updated pixels.
 22. Theat least one non-transitory computer readable storage medium method ofclaim 21, wherein each pixel value is to correspond to one of abrightness, a color, an intensity, or a distance.
 23. The at least onenon-transitory computer readable storage medium method of claim 21,wherein the computing device is to apply the segmented image in one ormore of object recognition, surveillance, or tracking.
 24. The at leastone non-transitory computer readable storage medium method of claim 21,wherein each class interval is to have a same width.
 25. The at leastone non-transitory computer readable storage medium of claim 21, whereinthe instructions, when executed, cause a computing device to: set theinitial mean of a class interval to a midpoint of the class interval;and compute the weighted mean using a weighting function based on anumber of pixels in a bin in the class interval.